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Abstract

Microcalcification (MC) detection is an important component of breast cancer diagnosis. However, visual analysis of mammograms is a difficult task for radiologists. Computer Aided Diagnosis (CAD) technology helps in identifying lesions and assists the radiologist in making his final decision. This work is a part of a CAD project carried out at the Imaging Science Research Division (ISRD), Digital Medical Imaging Program, Moffitt Cancer Research Center, Tampa, FL. A CAD system had been previously developed to perform the following tasks: (a) pre-processing, (b) segmentation and (c) feature extraction of mammogram images. Ten features covering spatial, and morphological domains were extracted from the mammograms and the samples were classified as Microcalcification (MC) or False alarm (False Positive microcalcification/ FP) based on a binary truth file obtained from a radiologist's initial investigation.

The main focus of this work was two-fold: (a) to analyze these features, select the most significant features among them and study their impact on classification accuracy and (b) to implement and compare two machine-learning algorithms, Neural Networks (NNs) and Support Vector Machines (SVMs) and evaluate their performances with these features. The NN was based on the Standard Back Propagation (SBP) algorithm. The SVM was implemented using polynomial, linear and Radial Basis Function (RBF) kernels. A detailed statistical analysis of the input features was performed. Feature selection was done using Stepwise Forward Selection (SFS) method. Training and testing of the classifiers was carried out using various training methods. Classifier evaluation was first performed with all the ten features in the model. Subsequently, only the features from SFS were used in the model to study their effect on classifier performance.

Accuracy assessment was done to evaluate classifier performance. Detailed statistical analysis showed that the given dataset showed poor discrimination between classes and proved a very difficult pattern recognition problem. The SVM performed better than the NN in most cases, especially on unseen data. No significant improvement in classifier performance was noted with feature selection. However, with SFS, the NN showed improved performance on unseen data. The training time taken by the SVM was several magnitudes less than the NN. Classifiers were compared on the basis of their accuracy and parameters like sensitivity and specificity. Free Receiver Operating Curves (FROCs) were used for evaluation of classifier performance. The highest accuracy observed was about 93% on training data and 76% for testing data with the SVM using Leave One Out (LOO) Cross Validation (CV) training.

Sensitivity was 81% and 46% on training and testing data respectively for a threshold of 0.7. The NN trained using the 'single test' method showed the highest accuracy of 86% on training data and 70% on testing data with respective sensitivity of 84% and 50%. Threshold in this case was -0.2. However, FROC analyses showed overall superiority of SVM especially on unseen data. Both spatial and morphological domain features were significant in our model. Features were selected based on their significance in the model. However, when tested with the NN and SVM, this feature selection procedure did not show significant improvement in classifier performance. It was interesting to note that the model with interactions between these selected variables showed excellent testing sensitivity with the NN classifier (about 81%). Recent research has shown SVMs outperform NNs in classification tasks.

SVMs show distinct advantages such as better generalization, increased speed of learning, ability to find a global optimum and ability to deal with linearly non-separable data. Thus, though NNs are more widely known and used, SVMs are expected to gain popularity in practical applications. Our findings show that the SVM outperforms the NN. However, its performance depends largely on the nature of data used.